Quantifying dimensional severity of obsessive-compulsive disorder for neurobiological research

Current research to explore genetic susceptibility factors in obsessive-compulsive disorder (OCD) has resulted in the tentative identification of a small number of genes. However, findings have not been readily replicated. It is now broadly accepted that a major limitation to this work is the heterogeneous nature of this disorder, and that an approach incorporating OCD symptom dimensions in a quantitative manner may be more successful in identifying both common as well as dimension-specific vulnerability genetic factors. As most existing genetic datasets did not collect specific dimensional severity ratings, a specific method to reliably extract dimensional ratings from the most widely used severity rating scale, the Yale-Brown Obsessive Compulsive Scale (YBOCS), for OCD is needed. This project aims to develop and validate a novel algorithm to extrapolate specific dimensional symptom severity ratings in OCD from the existing YBOCS for use in genetics and other neurobiological research. To accomplish this goal, we used a large data set comprising adult subjects from three independent sites: the Brazilian OCD Consortium, the Sunnybrook Health Sciences Centre in Toronto, Canada and the Hospital of Bellvitge, in Barcelona, Spain. A multinomial logistic regression was proposed to model and predict the quantitative phenotype [i.e., the severity of each of the five homogeneous symptom dimensions of the Dimensional YBOCS (DYBOCS)] in subjects who have only YBOCS (categorical) data. YBOCS and DYBOCS data obtained from 1183 subjects were used to build the model, which was tested with the leave-one-out cross-validation method. The model's goodness of fit, accepting a deviation of up to three points in the predicted DYBOCS score, varied from 78% (symmetry/order) to 84% (cleaning/contamination and hoarding dimensions). These results suggest that this algorithm may be a valuable tool for extracting dimensional phenotypic data for neurobiological studies in OCD.

Elucidation of photocatalysis, photoluminescence and antibacterial studies of ZnO thin films by spin coating method

The ZnO thin films have been prepared by spin coating followed by annealing at different temperatures like 300°C, 350°C, 400°C, 450°C, 500°C & 550°C and ZnO nanoparticles have been used for photocatalytic and antibacterial applications. The morphological investigation and phase analysis of synthesized thin films well characterized by X-ray diffraction (XRD), Field Emission Scanning Electron Microscopy (FESEM), Photoluminescence (PL), Transmission Electron Microscopy (TEM) and Raman studies. The luminescence peaks detected in the noticeable region between 350nm to 550nm for all synthesized nanosamples are associated to the existence of defects of oxygen sites. The luminescence emission bands are observed at 487nm (blue emission), and 530nm (green emission) at the RT. It is observed that there are no modification positions of PL peaks in all ZnO nanoparticles. In the current attempt, the synthesized ZnO particles have been used photocatalytic and antibacterial applications. The antibacterial activity of characterized samples was regulated using different concentrations of synthesized ZnO particles (100μg/ml, 200μg/ml, 300μg/ml, 400μg/ml, 500μg/ml and 600μg/ml) against gram positive and gram negative bacteria (S. pnemoniae, S. aureus, E. coli and E. hermannii) using agar well diffusion assay. The increase in concentration, decrease in zone of inhibition. The prepared ZnO morphologies showed photocatalytic activity under the sunlight enhancing the degradation rate of Rhodamine-B (RhB), which is one of the common water pollutant released by textile and paper industries.

Impact of automated ICA-based denoising of fMRI data in acute stroke patients

Different strategies have been developed using Independent Component Analysis (ICA) to automatically de-noise fMRI data, either focusing on removing only certain components (e.g. motion-ICA-AROMA, Pruim et al., 2015a) or using more complex classifiers to remove multiple types of noise components (e.g. FIX, Salimi-Khorshidi et al., 2014 Griffanti et al., 2014). However, denoising data obtained in an acute setting might prove challenging: the presence of multiple noise sources may not allow focused strategies to clean the data enough and the heterogeneity in the data may be so great to critically undermine complex approaches. The purpose of this study was to explore what automated ICA based approach would better cope with these limitations when cleaning fMRI data obtained from acute stroke patients. The performance of a focused classifier (ICA-AROMA) and a complex classifier (FIX) approaches were compared using data obtained from twenty consecutive acute lacunar stroke patients using metrics determining RSN identification, RSN reproducibility, changes in the BOLD variance, differences in the estimation of functional connectivity and loss of temporal degrees of freedom. The use of generic-trained FIX resulted in misclassification of components and significant loss of signal (< 80%), and was not explored further. Both ICA-AROMA and patient-trained FIX based denoising approaches resulted in significantly improved RSN reproducibility (p < 0.001), localized reduction in BOLD variance consistent with noise removal, and significant changes in functional connectivity (p < 0.001). Patient-trained FIX resulted in higher RSN identifiability (p < 0.001) and wider changes both in the BOLD variance and in functional connectivity compared to ICA-AROMA. The success of ICA-AROMA suggests that by focusing on selected components the full automation can deliver meaningful data for analysis even in population with multiple sources of noise. However, the time invested to train FIX with appropriate patient data proved valuable, particularly in improving the signal-to-noise ratio.

An unusual case of Escherichia coli O157:H7 infection with pseudomembranous colitis-like lesions associated with haemolytic-uraemic syndrome and neurological sequelae

A 75-year-old man was admitted with abdominal pain and fresh rectal bleeding. Significantly, he had no risk factors for Clostridium difficile infection. An abdominal CT demonstrated colonic thickening, and flexible sigmoidoscopy identified pseudomembranous colitis-like lesions. After initial treatment as C. difficile colitis, a stool sample revealed Escherichia coli O157:H7 infection. Antibiotic therapy was stopped due to the risk of lysis-mediated toxin release, but unfortunately, the patient continued to deteriorate. He developed several of the severe sequelae of E. coli O157:H7 infection, including haemolytic-uraemic syndrome with an acute kidney injury necessitating haemofiltration, plus progressively severe seizures requiring escalating antiepileptic treatment and intubation for airway protection. After a prolonged intensive care admission and subsequent recovery on the ward, our patient was discharged alive.

Characterisation of polyamide 11/copper antimicrobial composites for medical device applications

Direct incorporation of antimicrobial additive into the polymer matrix is a cost effective approach for the development of polymer/metal antimicrobial composites. Application of these antimicrobial composite systems for manufacturing medical devices addresses the issue of device related infections. In the present study, commercially available inorganic copper based additive, Plasticopper, was incorporated into a Polyamide 11(PA 11) matrix during the polymer processing stage. These polymer composites were evaluated for their morphological, mechanical, antimicrobial and ion release properties. Isothermal crystallisation studies showed that the copper additive acted as a nucleating agent and promoted faster crystallisation. Short term mechanical studies confirmed that the incorporation of copper has reinforcing effect on the composites with 5 and 10% copper loadings and did not adversely affect the short-term mechanical performance of the polymer composites. These composite systems were shown to be active against Escherichia coli ATCC 8739 with >99.99% reduction in bacterial population. Corresponding ion release profiles for these composites indicated long term antimicrobial activity.

Treatment-Resistant Schizophrenia: Treatment Response and Resistance in Psychosis (TRRIP) Working Group Consensus Guidelines on Diagnosis and Terminology

OBJECTIVE

Research and clinical translation in schizophrenia is limited by inconsistent definitions of treatment resistance and response. To address this issue, the authors evaluated current approaches and then developed consensus criteria and guidelines.

METHOD

A systematic review of randomized antipsychotic clinical trials in treatment-resistant schizophrenia was performed, and definitions of treatment resistance were extracted. Subsequently, consensus operationalized criteria were developed through 1) a multiphase, mixed methods approach, 2) identification of key criteria via an online survey, and 3) meetings to achieve consensus.

RESULTS

Of 2,808 studies identified, 42 met inclusion criteria. Of these, 21 studies (50%) did not provide operationalized criteria. In the remaining studies, criteria varied considerably, particularly regarding symptom severity, prior treatment duration, and antipsychotic dosage thresholds; only two studies (5%) utilized the same criteria. The consensus group identified minimum and optimal criteria, employing the following principles: 1) current symptoms of a minimum duration and severity determined by a standardized rating scale; 2) moderate or worse functional impairment; 3) prior treatment consisting of at least two different antipsychotic trials, each for a minimum duration and dosage; 4) systematic monitoring of adherence and meeting of minimum adherence criteria; 5) ideally at least one prospective treatment trial; and 6) criteria that clearly separate responsive from treatment-resistant patients.

CONCLUSIONS

There is considerable variation in current approaches to defining treatment resistance in schizophrenia. The authors present consensus guidelines that operationalize criteria for determining and reporting treatment resistance, adequate treatment, and treatment response, providing a benchmark for research and clinical translation.

Abstract P5-15-05: Randomized phase II study evaluating different schedules of nab-paclitaxel in metastatic breast cancer (MBC): Results of the SNAP study

Background

Longer chemotherapy (CT) duration is associated with a significant improvement in progression-free survival (PFS) and a moderate, but significant improvement in overall survival (OS) in MBC patients (pts). Prolonged CT administration, however, must be weighed against the side effects of continuous CT delivery. The SNAP trial was designed to improve the tolerability of prolonged CT by studying alternative treatment schedules.

Methods

The SNAP trial enrolled 258 women from April 2013 to Aug 2015. Eligibility criteria included HER2- MBC, no prior CT for advanced disease, measurable and/or non-measurable disease.

All eligible pts were randomized to one of three arms. Pts received the same induction chemotherapy consisting of 3 cycles of nab-Paclitaxel given days 1,8,15 Q28, followed by one of the three maintenance therapy schedules. Originally, the dose of the induction chemotherapy was 150 mg/m2, but this was reduced to 125 mg/m2 following the first safety review of 48 treated pts. The three schedules of nab-Paclitaxel used as maintenance therapy were (Arm A) nab-Paclitaxel 150 mg/m2 d 1,15 Q28; (Arm B) nab-Paclitaxel 100 mg/m2 d 1,8,15 Q28; (Arm C) nab-Paclitaxel 75 mg/m2 d 1,8,15,22 Q28.

The primary objective is to evaluate the efficacy of three nab-Paclitaxel regimens as measured by progression-free survival (PFS), using the historical reference of PFS (based on AVADO study) of docetaxel for first-line treatment of metastatic breast cancer. Each of the three regimens is compared to the historic 7-month median PFS to determine whether any of the three regimens are worthy of further investigation. Secondary endpoints include tolerability, feasibility, response rate, OS and QoL.

Results

Two-hundred-fifty-eight pts have been randomised and 255 are available for primary endpoint evaluation. At 18.2 months' median follow-up, 182 PFS events and 85 deaths have been observed. Median PFS was 7.9 months (90%CI 6.8-8.4) in Arm A, 9.0 months (90%CI 8.1-10.9) in Arm B and 8.5 (90%CI 6.7-9.5) in Arm C. PFS in Arm B was significantly longer than the historic PFS of first-line docetaxel (one-sided log-rank p=0.03).

As expected, neurotoxicity was the most frequent adverse event. In the induction phase, grade≥2 sensory neuropathy was reported in 14.8% of pts at the starting dose of 150 mg/m2 and 7.5% at the starting dose of 125 mg/m2; grade≥3 sensory neuropathy occurred in 2.5% and 0% of the pts, respectively. In the maintenance phase, grade≥2 sensory neuropathy was reported in 37.9% of pts in Arm A, 36.1% in Arm B and 31.2% in Arm C; grade≥3 sensory neuropathy occurred in 9.1%, 5.6% and 6.6% of the pts, respectively.

199 pts started the maintenance phase. The median number of maintenance cycles was 3, 4, and 5, respectively. Stopping maintenance for reasons other than objective progression occurred in 41%, 58%, and 53%, respectively.

Conclusion

The SNAP trial indicates that alternative maintenance chemotherapy schedules with reduced doses after a short term induction phase at conventional doses are feasible and significantly more active than the historical PFS of docetaxel in the first line treatment of advanced breast cancer.

Citation Format: Gennari A, Sun Z, Hasler-Strub U, Colleoni M, Kennedy J, von Moos R, Cortes J, Vidal M, Hennessy B, Walshe J, Amillano Parraga K, Morales Murrillo S, Pagani O, Barbeaux A, Borstnar S, Rabaglio M, Maibach R, Regan MM, Jerusalem G. Randomized phase II study evaluating different schedules of nab-paclitaxel in metastatic breast cancer (MBC): Results of the SNAP study [abstract]. In: Proceedings of the 2016 San Antonio Breast Cancer Symposium; 2016 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2017;77(4 Suppl):Abstract nr P5-15-05.

Abstract TP44: Quantifying Edema in Acute Stroke

Introduction: The Acute Stroke Imaging Roadmap III identifies structural distortion due to vasogenic edema and hemorrhage as a research priority for defining final infarction. Non-linear registration (NLR) of a follow up scan to an undistorted presenting scan could correct for distortions due to edema, hemorrhage or atrophy, achieving this goal. In addition, the difference between the volume of infarction following NLR and the volume following a rigid body registration (RBR) reflects the degree of anatomical distortion. In this study we evaluate this technique to correct for subacute edema at different timepoints, and generate a metric to quantify brain swelling at these times. We determine whether early edema at 24 hours predicts edema at 1 week.

Methods: Patients with non-lacunar ischemic stroke were recruited into a MRI study. Patients had structural T1-weighted, T2-weighted FLAIR and diffusion-weighted imaging (DWI, b=1000/0) at presentation, 24hrs, 1wk and 1mo. Infarction was defined manually at 24hrs using DWI, and at 1wk and 1mo using FLAIR image by 2 raters. To quantify edema, both NLR warps and RBR matrices were generated between the T1 images at each timepoint to the presenting T1 scan. Infarct masks were transformed to presenting image space using RBR and NLR, and the relative difference in volumes used to quantify the Edema Metric (EM).

Results: 34 patients were recruited into the study. NLR corrected for distortions due to edema and hemorrhagic transformation at the 24hr and 1wk timepoints. The EM at 24 hours, 1 week and 1 month were 17.7% (p=0.009), 26.5% (p=0.02), and 7.1% (p=0.05) respectively for the manually defined infarct masks. EM at 24 hours predicted edema at 1 week (r 2 =37%, p=0.009), but not at 1 month (r 2 =3%, p=0.6).

Conclusions: NLR provides an opportunity to correct for edema at subacute timepoints and by comparing infarct volumes to those following RBR provides a measure of edema. The EM quantifies the contribution of edema at 24hrs and 1wk, and potentially allows the selection of patients at 24hrs who are likely to develop significant swelling at 1 week. The EM may also be useful in stroke trials to quantify the effect sizes of treatments aimed at minimizing edema in stroke.

Optimizing image registration and infarct definition in stroke research

OBJECTIVE

Accurate representation of final infarct volume is essential for assessing the efficacy of stroke interventions in imaging-based studies. This study defines the impact of image registration methods used at different timepoints following stroke, and the implications for infarct definition in stroke research.

METHODS

Patients presenting with acute ischemic stroke were imaged serially using magnetic resonance imaging. Infarct volume was defined manually using four metrics: 24-h b1000 imaging; 1-week and 1-month T2-weighted FLAIR; and automatically using predefined thresholds of ADC at 24 h. Infarct overlap statistics and volumes were compared across timepoints following both rigid body and nonlinear image registration to the presenting MRI. The effect of nonlinear registration on a hypothetical trial sample size was calculated.

RESULTS

Thirty-seven patients were included. Nonlinear registration improved infarct overlap statistics and consistency of total infarct volumes across timepoints, and reduced infarct volumes by 4.0 mL (13.1%) and 7.1 mL (18.2%) at 24 h and 1 week, respectively, compared to rigid body registration. Infarct volume at 24 h, defined using a predetermined ADC threshold, was less sensitive to infarction than b1000 imaging. 1-week T2-weighted FLAIR imaging was the most accurate representation of final infarct volume. Nonlinear registration reduced hypothetical trial sample size, independent of infarct volume, by an average of 13%.

INTERPRETATION

Nonlinear image registration may offer the opportunity of improving the accuracy of infarct definition in serial imaging studies compared to rigid body registration, helping to overcome the challenges of anatomical distortions at subacute timepoints, and reducing sample size for imaging-based clinical trials.

The Drosophila foraging gene human orthologue PRKG1 predicts individual differences in the effects of early adversity on maternal sensitivity

There is variation in the extent to which childhood adverse experience affects adult individual differences in maternal behavior. Genetic variation in the animal foraging gene, which encodes a cGMP-dependent protein kinase, contributes to variation in the responses of adult fruit flies, Drosophila melanogaster, to early life adversity and is also known to play a role in maternal behavior in social insects. Here we investigate genetic variation in the human foraging gene (PRKG1) as a predictor of individual differences in the effects of early adversity on maternal behavior in two cohorts. We show that the PRKG1 genetic polymorphism rs2043556 associates with maternal sensitivity towards their infants. We also show that rs2043556 moderates the association between self-reported childhood adversity of the mother and her later maternal sensitivity. Mothers with the TT allele of rs2043556 appeared buffered from the effects of early adversity, whereas mothers with the presence of a C allele were not. Our study used the Toronto Longitudinal Cohort (N=288 mother-16 month old infant pairs) and the Maternal Adversity and Vulnerability and Neurodevelopment Cohort (N=281 mother-18 month old infant pairs). Our findings expand the literature on the contributions of both genetics and gene-environment interactions to maternal sensitivity, a salient feature of the early environment relevant for child neurodevelopment.

Quantification of Serial Cerebral Blood Flow in Acute Stroke Using Arterial Spin Labeling

Supplemental Digital Content is available in the text.

Background and Purpose—

Perfusion-weighted imaging is used to select patients with acute ischemic stroke for intervention, but knowledge of cerebral perfusion can also inform the understanding of ischemic injury. Arterial spin labeling allows repeated measurement of absolute cerebral blood flow (CBF) without the need for exogenous contrast. The aim of this study was to explore the relationship between dynamic CBF and tissue outcome in the month after stroke onset.

Methods—

Patients with nonlacunar ischemic stroke underwent ≤5 repeated magnetic resonance imaging scans at presentation, 2 hours, 1 day, 1 week, and 1 month. Imaging included vessel-encoded pseudocontinuous arterial spin labeling using multiple postlabeling delays to quantify CBF in gray matter regions of interest. Receiver–operator characteristic curves were used to predict tissue outcome using CBF. Repeatability was assessed in 6 healthy volunteers and compared with contralateral regions of patients. Diffusion-weighted and T2-weighted fluid attenuated inversion recovery imaging were used to define tissue outcome.

Results—

Forty patients were included. In contralateral regions of patients, there was significant variation of CBF between individuals, but not between scan times (mean±SD: 53±42 mL/100 g/min). Within ischemic regions, mean CBF was lowest in ischemic core (17±23 mL/100 g/min), followed by regions of early (21±26 mL/100 g/min) and late infarct growth (25±35 mL/100 g/min; ANOVA P<0.0001). Between patients, there was marked overlap in presenting and serial CBF values.

Conclusions—

Knowledge of perfusion dynamics partially explained tissue fate. Factors such as metabolism and tissue susceptibility are also likely to influence tissue outcome.